Abstract: This disclosure is directed to an enhanced audio file generator. One aspect is a method of enhancing input speech in an input audio file, the method comprising receiving the input audio file representing the input speech, wherein the input audio file is recorded at an audio recording device, and generating an enhanced audio file by applying an audio transformation model to the input audio file, wherein applying the audio transformation model to generate the enhanced audio file comprises extracting parameters defining audio features from the input audio file, the parameters including a noise parameter defining noise in the input audio file and one or more other preset parameters respectively defining other audio features, synthesizing clean speech based on the extracted parameters including the noise parameter, wherein synthesizing the clean speech comprises transforming the noise parameter to defined value(s); and generating the enhanced audio file with the synthesized clean speech.

Abstract: A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.

Abstract: A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.

Abstract: A method includes displaying a user interface of a digital audio workstation, which includes a composition region for generating a composition. The composition region includes a representation of a first MIDI file that has already been added to the composition by a user. The method further includes receiving a user input to import, into the composition region, an audio file. In response to the user input to import the audio file, the method includes importing the audio file, which includes, without user intervention, aligning the audio file with a rhythm of the first MIDI file, modifying a rhythm of the audio file based on the rhythm of the first MIDI file, and displaying a representation of the audio file in the composition region.

Abstract: Term masking is performed by generating a time-alignment value for a plurality of units of sound in vocal audio content contained in a mixed audio track, force-aligning each of the plurality of units of sound to the vocal audio content based on the time-alignment value, thereby generating a plurality of force-aligned identifiable units of sound, identifying from the plurality of force-aligned units of sound a force-aligned unit of sound to be altered, and altering the identified force-aligned unit of sound.

Abstract: Term masking is performed by generating a time-alignment value for a plurality of identifiable units of sound in vocal audio content contained in a mixed audio track, force-aligning each of the plurality of identifiable units of sound to the vocal audio content based on the time-alignment value, thereby generating a plurality of force-aligned identifiable units of sound, identifying from the plurality of force-aligned identifiable units of sound a force-aligned identifiable unit of sound to be muddled, and audio muddling the force-aligned identifiable unit of sound to be muddled.

Abstract: Term masking is performed by generating a time-alignment value for a plurality of identifiable units of sound in vocal audio content contained in a mixed audio track, force-aligning each of the plurality of identifiable units of sound to the vocal audio content based on the time-alignment value, thereby generating a plurality of force-aligned identifiable units of sound, identifying from the plurality of force-aligned identifiable units of sound a force-aligned identifiable unit of sound to be muddled, and audio muddling the force-aligned identifiable unit of sound to be muddled.

Abstract: Term masking is performed by generating a time-alignment value for a plurality of identifiable units of sound in vocal audio content contained in a mixed audio track, force-aligning each of the plurality of identifiable units of sound to the vocal audio content based on the time-alignment value, thereby generating a plurality of force-aligned identifiable units of sound, identifying from the plurality of force-aligned identifiable units of sound a force-aligned identifiable unit of sound to be muddled, and audio muddling the force-aligned identifiable unit of sound to be muddled.

Abstract: A cuepoint determination system utilizes a convolutional neural network (CNN) to determine cuepoint placements within media content items to facilitate smooth transitions between them. For example, audio content from a media content item is normalized to a plurality of beats, the beats are partitioned into temporal sections, and acoustic feature groups are extracted from each beat in one or more of the temporal sections. The acoustic feature groups include at least downbeat confidence, position in bar, peak loudness, timbre and pitch. The extracted acoustic feature groups for each beat are provided as input to the CNN on a per temporal section basis to predict whether a beat immediately following the temporal section within the media content item is a candidate for cuepoint placement. A cuepoint placement is then determined from among the candidate cuepoint placements predicted by the CNN.